Lessons from the Texas Homeowners Insurance Crisis Bob Puelz ...

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Figure 3 illustrates the change in statewide dollar losses for property perils over time scaled for total statewide insurance company exposure. While fire, theft, VMM, liability and the other peril category have shown only modest changes, wind and water perils are much more variable with the water peril exhibiting a substantially increasing rate of change from 1999 to 2001. Given that the dataset is detailed by county, it is possible to explore how a particular county’s by-peril distribution of losses is contrasted with the overall state. That is, the fire, wind, water and theft perils loss costs compared to total loss costs suffered by county residents for a given year. This historical pattern of losses is benchmarked against Texas statewide proportions so that county constituents can assess how their actual experience has faired relative to statewide averages, although it is important to note that geographical location brings the magnitude of risks to bear across counties differentially. As an example, Figure 4 provides a graphical representation of four major property perils for Harris County, Texas (the Houston area) because, according to the U.S. Census Bureau, it is the largest county in the state. 15 Moving vertically, the first four charts show the dollar proportion of losses by peril for Harris County relative to the statewide averages of Texas’s 254 counties. The bottom chart in Figure 4 is a view of Harris County’s homeowners total paid losses relative to total premiums paid, or a paid loss ratio. While this measure incorporates only paid benefits received by consumers from the homeowners contract relative to the costs (the premiums paid), it excludes the administrative costs of the homeowners product and reserves set aside by insurers for losses not yet paid; therefore, it is an upwardly biased measure of insurer profitability. 16 15 A similar analysis has been done on each of the 254 Texas counties and can be obtained from the author upon request. 16 The by-peril annual data represent paid losses and not incurred losses. Still, from an insurance management view, the information can be useful for identifying problems or opportunities, hence strategy. 12
[Figure 4 about here] One glimpse into the sources of rising claims is to focus on the water peril because problems associated with mold were the “hot button” insurance issue in Texas. The value of a paid water loss per policy by county was considered in 1996 and in 2001 and the percentage change calculated for each county. While a few, particularly small, counties had no water losses in either 1996 or 2001 and, thus, were removed from consideration in Figure 5, 246 of Texas’ 254 counties remained for this descriptive analysis. The cumulative distribution histogram reveals a spread in the water loss value changes, and the notable fact that about two-thirds of Texas counties experienced at least a 100% increase in the value of a paid water loss on a per policy basis. By contrast, a similar analysis for the fire peril indicates that about two-thirds of Texas counties had a change in the value of a fire loss per policy of up to 25%, with more than half of the counties showing a negative change in value from 1996 to 2001. V. Hypothesis Testing and Empirical Results [Figure 5 about here] To obtain a clearer understanding about the Texas homeowners market, the questions addressed in this study need an enhanced analytical framework. The work of Klein and Grace (2001) provides a useful perspective for empirical testing in homeowners insurance markets; in particular, their clarity on the issue of a broader empirical framework for analysis that overcomes myopic inferences that can be made from examining data in only a tabular, univariate format. 17 17 Klein and Grace (2001) provide a rigorous examination of red-lining in the Texas homeowners insurance market and find loss ratios “roughly equivalent” between ZIP codes distinguished by low and high minority representation; an extension of the work by Harrington and Niehaus (1998) in urban automobile insurance markets. In their econometric discussion, they raise the issue of omitted variable bias; in this paper, the potential bias could 13
Page 1 and 2: Lessons from the Texas Homeowners I
Page 3 and 4: I. Introduction Lessons from the Te
Page 5 and 6: Illinois with company-level data to
Page 7 and 8: [Table 1 about here] might have bee
Page 9 and 10: with the NAIC, and were sourced for
Page 11 and 12: To further explore the Texas settin
Page 13: amounts are summarized in Table 4.
Page 17 and 18: the average household size of an oc
Page 19 and 20: units that have been classified as
Page 21 and 22: as equations in levels. The Hansen
Page 23 and 24: while variations in perils help exp
Page 26 and 27: Table 1 Texas Homeowners Premiums W
Page 28 and 29: "Combined" Ratio $1.80 $1.60 $1.40
Page 30 and 31: Table 5 Texas Statewide Homeowners
Page 32 and 33: 35% 30% 25% 20% 15% 10% 5% 0% 60% 5
Page 34 and 35: Cumulative % of Texas Counties 100%
Page 36 and 37: Table 7 GMM Estimates of Empirical
Page 38: Harrington, Scott E. and Greg Nieha

Lessons from the Texas Homeowners Insurance Crisis Bob Puelz ...

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